Housing Rental Analysis for San Francisco¶

In this challenge, your job is to use your data visualisation skills, including aggregation, interactive visualisations, and geospatial analysis, to find properties in the San Francisco market that are viable investment opportunities.

Instructions¶

Use the san_francisco_housing.ipynb notebook to visualise and analyse the real-estate data.

Note that this assignment requires you to create a visualisation by using hvPlot and GeoViews. Additionally, you need to read the sfo_neighborhoods_census_data.csv file from the Resources folder into the notebook and create the DataFrame that you’ll use in the analysis.

The main task in this Challenge is to visualise and analyse the real-estate data in your Jupyter notebook. Use the san_francisco_housing.ipynb notebook to complete the following tasks:

  • Calculate and plot the housing units per year.

  • Calculate and plot the average prices per square foot.

  • Compare the average prices by neighbourhood.

  • Build an interactive neighbourhood map.

  • Compose your data story.

Calculate and Plot the Housing Units per Year¶

For this part of the assignment, use numerical and visual aggregation to calculate the number of housing units per year, and then visualise the results as a bar chart. To do so, complete the following steps:

  1. Use the groupby function to group the data by year. Aggregate the results by the mean of the groups.

  2. Use the hvplot function to plot the housing_units_by_year DataFrame as a bar chart. Make the x-axis represent the year and the y-axis represent the housing_units.

  3. Style and format the line plot to ensure a professionally styled visualisation.

  4. Note that your resulting plot should appear similar to the following image:

    A screenshot depicts an example of the resulting bar chart.

  5. Answer the following question:

    • What’s the overall trend in housing units over the period that you’re analysing?

Calculate and Plot the Average Sale Prices per Square Foot¶

For this part of the assignment, use numerical and visual aggregation to calculate the average prices per square foot, and then visualise the results as a bar chart. To do so, complete the following steps:

  1. Group the data by year, and then average the results. What’s the lowest gross rent that’s reported for the years that the DataFrame includes?

  2. Create a new DataFrame named prices_square_foot_by_year by filtering out the “housing_units” column. The new DataFrame should include the averages per year for only the sale price per square foot and the gross rent.

  3. Use hvPlot to plot the prices_square_foot_by_year DataFrame as a line plot.

    Hint This single plot will include lines for both sale_price_sqr_foot and gross_rent.

  4. Style and format the line plot to ensure a professionally styled visualisation.

  5. Note that your resulting plot should appear similar to the following image:

    A screenshot depicts an example of the resulting plot.

  6. Use both the prices_square_foot_by_year DataFrame and interactive plots to answer the following questions:

    • Did any year experience a drop in the average sale price per square foot compared to the previous year?

    • If so, did the gross rent increase or decrease during that year?

Compare the Average Sale Prices by Neighbourhood¶

For this part of the assignment, use interactive visualisations and widgets to explore the average sale price per square foot by neighbourhood. To do so, complete the following steps:

  1. Create a new DataFrame that groups the original DataFrame by year and neighbourhood. Aggregate the results by the mean of the groups.

  2. Filter out the “housing_units” column to create a DataFrame that includes only the sale_price_sqr_foot and gross_rent averages per year.

  3. Create an interactive line plot with hvPlot that visualises both sale_price_sqr_foot and gross_rent. Set the x-axis parameter to the year (x="year"). Use the groupby parameter to create an interactive widget for neighbourhood.

  4. Style and format the line plot to ensure a professionally styled visualisation.

  5. Note that your resulting plot should appear similar to the following image:

    A screenshot depicts an example of the resulting plot.

  6. Use the interactive visualisation to answer the following question:

    • For the Anza Vista neighbourhood, is the average sale price per square foot for 2016 more or less than the price that’s listed for 2012?

Build an Interactive Neighbourhood Map¶

For this part of the assignment, explore the geospatial relationships in the data by using interactive visualisations with hvPlot and GeoViews. To build your map, use the sfo_data_df DataFrame (created during the initial import), which includes the neighbourhood location data with the average prices. To do all this, complete the following steps:

  1. Read the neighbourhood_coordinates.csv file from the Resources folder into the notebook, and create a DataFrame named neighbourhood_locations_df. Be sure to set the index_col of the DataFrame as “Neighbourhood”.

  2. Using the original sfo_data_df Dataframe, create a DataFrame named all_neighbourhood_info_df that groups the data by neighbourhood. Aggregate the results by the mean of the group.

  3. Review the two code cells that concatenate the neighbourhood_locations_df DataFrame with the all_neighbourhood_info_df DataFrame. Note that the first cell uses the Pandas concat function to create a DataFrame named all_neighbourhoods_df. The second cell cleans the data and sets the “Neighbourhood” column. Be sure to run these cells to create the all_neighbourhoods_df DataFrame, which you’ll need to create the geospatial visualisation.

  4. Using hvPlot with GeoViews enabled, create a points plot for the all_neighbourhoods_df DataFrame. Be sure to do the following:

    • Set the size parameter to “sale_price_sqr_foot”.

    • Set the color parameter to “gross_rent”.

    • Set the frame_width parameter to 700.

    • Set the frame_height parameter to 500.

    • Include a descriptive title.

      Note that your resulting plot should appear similar to the following image:

      A screenshot depicts an example of a scatter plot created with hvPlot and GeoViews.

  5. Use the interactive map to answer the following question:

    • Which neighbourhood has the highest gross rent, and which has the highest sale price per square foot?

Compose Your Data Story¶

Based on the visualisations that you created, answer the following questions:

  • How does the trend in rental income growth compare to the trend in sales prices? Does this same trend hold true for all the neighbourhoods across San Francisco?

  • What insights can you share with your company about the potential one-click, buy-and-rent strategy that they're pursuing? Do neighbourhoods exist that you would suggest for investment, and why?

In [1]:
# Import the required libraries and dependencies
import pandas as pd
import hvplot.pandas
from pathlib import Path

Import the data¶

In [2]:
# Using the read_csv function and Path module, create a DataFrame 
# by importing the sfo_neighbourhoods_census_data.csv file from the Resources folder
sfo_data_df = pd.read_csv(Path("./Resources/sfo_neighbourhoods_census_data.csv"))

# Review the first and last five rows of the DataFrame
display(sfo_data_df.head())
display(sfo_data_df.tail())
year neighbourhood sale_price_sqr_foot housing_units gross_rent
0 2010 Alamo Square 291.182945 372560 1239
1 2010 Anza Vista 267.932583 372560 1239
2 2010 Bayview 170.098665 372560 1239
3 2010 Buena Vista Park 347.394919 372560 1239
4 2010 Central Richmond 319.027623 372560 1239
year neighbourhood sale_price_sqr_foot housing_units gross_rent
392 2016 Telegraph Hill 903.049771 384242 4390
393 2016 Twin Peaks 970.085470 384242 4390
394 2016 Van Ness/ Civic Center 552.602567 384242 4390
395 2016 Visitacion Valley 328.319007 384242 4390
396 2016 Westwood Park 631.195426 384242 4390

Calculate and Plot the Housing Units per Year¶

For this part of the assignment, use numerical and visual aggregation to calculate the number of housing units per year, and then visualise the results as a bar chart. To do so, complete the following steps:

  1. Use the groupby function to group the data by year. Aggregate the results by the mean of the groups.

  2. Use the hvplot function to plot the housing_units_by_year DataFrame as a bar chart. Make the x-axis represent the year and the y-axis represent the housing_units.

  3. Style and format the line plot to ensure a professionally styled visualisation.

  4. Note that your resulting plot should appear similar to the following image:

    A screenshot depicts an example of the resulting bar chart.

  5. Answer the following question:

    • What’s the overall trend in housing units over the period that you’re analysing?

Step 1: Use the groupby function to group the data by year. Aggregate the results by the mean of the groups.¶

In [3]:
# Create a numerical aggregation that groups the data by the year and then averages the results.
housing_units_by_year = sfo_data_df.groupby('year').mean()

# Review the DataFrame
housing_units_by_year
Out[3]:
sale_price_sqr_foot housing_units gross_rent
year
2010 369.344353 372560.0 1239.0
2011 341.903429 374507.0 1530.0
2012 399.389968 376454.0 2324.0
2013 483.600304 378401.0 2971.0
2014 556.277273 380348.0 3528.0
2015 632.540352 382295.0 3739.0
2016 697.643709 384242.0 4390.0

Step 2: Use the hvplot function to plot the housing_units_by_year DataFrame as a bar chart. Make the x-axis represent the year and the y-axis represent the housing_units.¶

Step 3: Style and format the line plot to ensure a professionally styled visualisation.¶

In [35]:
# Create a visual aggregation explore the housing units by year
housing_units_by_year['housing_units'].hvplot.bar(
    x='year',
    y='housing_units',
    xlabel='Year',
    ylabel='Housing Units',
    title='Housing Units in San Francisco from 2010 to 2016'
).opts(
    yformatter='%.0f',
    color='skyblue',
    hover_color='orange'
)
Out[35]:

Step 5: Answer the following question:¶

Question: What is the overall trend in housing_units over the period being analysed?

Answer: The housing units over the market in San Francisco is continuously increasing year over year starting from 2010 to 2016.

Calculate and Plot the Average Sale Prices per Square Foot¶

For this part of the assignment, use numerical and visual aggregation to calculate the average prices per square foot, and then visualise the results as a bar chart. To do so, complete the following steps:

  1. Group the data by year, and then average the results. What’s the lowest gross rent that’s reported for the years that the DataFrame includes?

  2. Create a new DataFrame named prices_square_foot_by_year by filtering out the “housing_units” column. The new DataFrame should include the averages per year for only the sale price per square foot and the gross rent.

  3. Use hvPlot to plot the prices_square_foot_by_year DataFrame as a line plot.

    Hint This single plot will include lines for both sale_price_sqr_foot and gross_rent.

  4. Style and format the line plot to ensure a professionally styled visualisation.

  5. Note that your resulting plot should appear similar to the following image:

    A screenshot depicts an example of the resulting plot.

  6. Use both the prices_square_foot_by_year DataFrame and interactive plots to answer the following questions:

    • Did any year experience a drop in the average sale price per square foot compared to the previous year?

    • If so, did the gross rent increase or decrease during that year?

Step 1: Group the data by year, and then average the results.¶

In [5]:
# Create a numerical aggregation by grouping the data by year and averaging the results
prices_square_foot_by_year = sfo_data_df.groupby('year').mean()

# Review the resulting DataFrame
prices_square_foot_by_year
Out[5]:
sale_price_sqr_foot housing_units gross_rent
year
2010 369.344353 372560.0 1239.0
2011 341.903429 374507.0 1530.0
2012 399.389968 376454.0 2324.0
2013 483.600304 378401.0 2971.0
2014 556.277273 380348.0 3528.0
2015 632.540352 382295.0 3739.0
2016 697.643709 384242.0 4390.0

Question: What is the lowest gross rent reported for the years included in the DataFrame?

Answer: The lowest gross rent is $1239 in the year of 2010

Step 2: Create a new DataFrame named prices_square_foot_by_year by filtering out the “housing_units” column. The new DataFrame should include the averages per year for only the sale price per square foot and the gross rent.¶

In [6]:
# Filter out the housing_units column, creating a new DataFrame 
# Keep only sale_price_sqr_foot and gross_rent averages per year
prices_square_foot_by_year = prices_square_foot_by_year[['sale_price_sqr_foot', 'gross_rent']]

# Review the DataFrame
prices_square_foot_by_year
Out[6]:
sale_price_sqr_foot gross_rent
year
2010 369.344353 1239.0
2011 341.903429 1530.0
2012 399.389968 2324.0
2013 483.600304 2971.0
2014 556.277273 3528.0
2015 632.540352 3739.0
2016 697.643709 4390.0

Step 3: Use hvPlot to plot the prices_square_foot_by_year DataFrame as a line plot.¶

Hint This single plot will include lines for both sale_price_sqr_foot and gross_rent

Step 4: Style and format the line plot to ensure a professionally styled visualisation.¶

In [7]:
# Plot prices_square_foot_by_year. 
# Inclued labels for the x- and y-axes, and a title.
prices_square_foot_by_year.hvplot(
    xlabel='Year',
    ylabel='Gross Rent/Sale Price Per Square Foot',
    title='Sale Price Per Square Foot and Average Gross Rent - 2010-2016 - San Francisco')
Out[7]:

Step 6: Use both the prices_square_foot_by_year DataFrame and interactive plots to answer the following questions:¶

Question: Did any year experience a drop in the average sale price per square foot compared to the previous year?

Answer: In 2011, the average sale price per square foot decreased compared with 2010.

Question: If so, did the gross rent increase or decrease during that year?

Answer: However, the gross rent increase in 2011 compared with 2010.

Compare the Average Sale Prices by Neighbourhood¶

For this part of the assignment, use interactive visualisations and widgets to explore the average sale price per square foot by neighbourhood. To do so, complete the following steps:

  1. Create a new DataFrame that groups the original DataFrame by year and neighbourhood. Aggregate the results by the mean of the groups.

  2. Filter out the “housing_units” column to create a DataFrame that includes only the sale_price_sqr_foot and gross_rent averages per year.

  3. Create an interactive line plot with hvPlot that visualises both sale_price_sqr_foot and gross_rent. Set the x-axis parameter to the year (x="year"). Use the groupby parameter to create an interactive widget for neighbourhood.

  4. Style and format the line plot to ensure a professionally styled visualisation.

  5. Note that your resulting plot should appear similar to the following image:

    A screenshot depicts an example of the resulting plot.

  6. Use the interactive visualisation to answer the following question:

    • For the Anza Vista neighbourhood, is the average sale price per square foot for 2016 more or less than the price that’s listed for 2012?

Step 1: Create a new DataFrame that groups the original DataFrame by year and neighbourhood. Aggregate the results by the mean of the groups.¶

In [8]:
# Group by year and neighbourhood and then create a new dataframe of the mean values
prices_by_year_by_neighbourhood = sfo_data_df.groupby(['year', 'neighbourhood']).mean()

# Review the DataFrame
prices_by_year_by_neighbourhood.head()
Out[8]:
sale_price_sqr_foot housing_units gross_rent
year neighbourhood
2010 Alamo Square 291.182945 372560.0 1239.0
Anza Vista 267.932583 372560.0 1239.0
Bayview 170.098665 372560.0 1239.0
Buena Vista Park 347.394919 372560.0 1239.0
Central Richmond 319.027623 372560.0 1239.0

Step 2: Filter out the “housing_units” column to create a DataFrame that includes only the sale_price_sqr_foot and gross_rent averages per year.¶

In [9]:
# Filter out the housing_units
prices_by_year_by_neighbourhood = prices_by_year_by_neighbourhood[['sale_price_sqr_foot', 'gross_rent']]

# Review the first and last five rows of the DataFrame
display(prices_by_year_by_neighbourhood.head())
display(prices_by_year_by_neighbourhood.tail())
sale_price_sqr_foot gross_rent
year neighbourhood
2010 Alamo Square 291.182945 1239.0
Anza Vista 267.932583 1239.0
Bayview 170.098665 1239.0
Buena Vista Park 347.394919 1239.0
Central Richmond 319.027623 1239.0
sale_price_sqr_foot gross_rent
year neighbourhood
2016 Telegraph Hill 903.049771 4390.0
Twin Peaks 970.085470 4390.0
Van Ness/ Civic Center 552.602567 4390.0
Visitacion Valley 328.319007 4390.0
Westwood Park 631.195426 4390.0

Step 3: Create an interactive line plot with hvPlot that visualises both sale_price_sqr_foot and gross_rent. Set the x-axis parameter to the year (x="year"). Use the groupby parameter to create an interactive widget for neighbourhood.¶

Step 4: Style and format the line plot to ensure a professionally styled visualisation.¶

In [40]:
# Use hvplot to create an interactive line plot of the average price per square foot
# The plot should have a dropdown selector for the neighbourhood
prices_by_year_by_neighbourhood.hvplot(
    groupby='neighbourhood',
    x='year',
    xlabel='Year',
    ylabel='Gross Rent/Sales Price Per Square Foot',
    title='Sale Price Per Square Foot and Average Gross Rent - 2010-2016 - By Neighborhood')
Out[40]:

Step 6: Use the interactive visualisation to answer the following question:¶

Question: For the Anza Vista neighbourhood, is the average sale price per square foot for 2016 more or less than the price that’s listed for 2012?

Answer: For Anza Vista neighbourhood, average sale price per square foot for 2016 is lower than the price listed for 2012.

Build an Interactive Neighbourhood Map¶

For this part of the assignment, explore the geospatial relationships in the data by using interactive visualisations with hvPlot and GeoViews. To build your map, use the sfo_data_df DataFrame (created during the initial import), which includes the neighbourhood location data with the average prices. To do all this, complete the following steps:

  1. Read the neighbourhood_coordinates.csv file from the Resources folder into the notebook, and create a DataFrame named neighbourhood_locations_df. Be sure to set the index_col of the DataFrame as “Neighbourhood”.

  2. Using the original sfo_data_df Dataframe, create a DataFrame named all_neighbourhood_info_df that groups the data by neighbourhood. Aggregate the results by the mean of the group.

  3. Review the two code cells that concatenate the neighbourhood_locations_df DataFrame with the all_neighbourhood_info_df DataFrame. Note that the first cell uses the Pandas concat function to create a DataFrame named all_neighbourhoods_df. The second cell cleans the data and sets the “Neighbourhood” column. Be sure to run these cells to create the all_neighbourhoods_df DataFrame, which you’ll need to create the geospatial visualisation.

  4. Using hvPlot with GeoViews enabled, create a points plot for the all_neighbourhoods_df DataFrame. Be sure to do the following:

    • Set the size parameter to “sale_price_sqr_foot”.

    • Set the color parameter to “gross_rent”.

    • Set the frame_width parameter to 700.

    • Set the frame_height parameter to 500.

    • Include a descriptive title.

      Note that your resulting plot should appear similar to the following image:

      A screenshot depicts an example of a scatter plot created with hvPlot and GeoViews.

  5. Use the interactive map to answer the following question:

    • Which neighbourhood has the highest gross rent, and which has the highest sale price per square foot?

Step 1: Read the neighbourhood_coordinates.csv file from the Resources folder into the notebook, and create a DataFrame named neighbourhood_locations_df. Be sure to set the index_col of the DataFrame as “Neighbourhood”.¶

In [16]:
# Load neighbourhoods coordinates data
neighbourhood_locations_df = pd.read_csv(Path("./Resources/neighbourhoods_coordinates.csv"), index_col='Neighbourhood')

# Review the DataFrame
neighbourhood_locations_df.head()
Out[16]:
Lat Lon
Neighbourhood
Alamo Square 37.791012 -122.402100
Anza Vista 37.779598 -122.443451
Bayview 37.734670 -122.401060
Bayview Heights 37.728740 -122.410980
Bernal Heights 37.728630 -122.443050

Step 2: Using the original sfo_data_df Dataframe, create a DataFrame named all_neighbourhood_info_df that groups the data by neighbourhood. Aggregate the results by the mean of the group.¶

In [19]:
# Calculate the mean values for each neighbourhood
all_neighbourhood_info_df = sfo_data_df.groupby('neighbourhood').mean().drop(columns='year')

# Review the resulting DataFrame
all_neighbourhood_info_df.head()
Out[19]:
sale_price_sqr_foot housing_units gross_rent
neighbourhood
Alamo Square 366.020712 378401.0 2817.285714
Anza Vista 373.382198 379050.0 3031.833333
Bayview 204.588623 376454.0 2318.400000
Bayview Heights 590.792839 382295.0 3739.000000
Bernal Heights 576.746488 379374.5 3080.333333

Step 3: Review the two code cells that concatenate the neighbourhood_locations_df DataFrame with the all_neighbourhood_info_df DataFrame.¶

Note that the first cell uses the Pandas concat function to create a DataFrame named all_neighbourhoods_df.

The second cell cleans the data and sets the “Neighbourhood” column.

Be sure to run these cells to create the all_neighbourhoods_df DataFrame, which you’ll need to create the geospatial visualisation.

In [20]:
# Using the Pandas `concat` function, join the 
# neighbourhood_locations_df and the all_neighbourhood_info_df DataFrame
# The axis of the concatenation is "columns".
# The concat function will automatially combine columns with
# identical information, while keeping the additional columns.
all_neighbourhoods_df = pd.concat(
    [neighbourhood_locations_df, all_neighbourhood_info_df], 
    axis="columns",
    sort=False
)

# Review the resulting DataFrame
display(all_neighbourhoods_df.head())
display(all_neighbourhoods_df.tail())
Lat Lon sale_price_sqr_foot housing_units gross_rent
Alamo Square 37.791012 -122.402100 366.020712 378401.0 2817.285714
Anza Vista 37.779598 -122.443451 373.382198 379050.0 3031.833333
Bayview 37.734670 -122.401060 204.588623 376454.0 2318.400000
Bayview Heights 37.728740 -122.410980 590.792839 382295.0 3739.000000
Bernal Heights 37.728630 -122.443050 NaN NaN NaN
Lat Lon sale_price_sqr_foot housing_units gross_rent
Yerba Buena 37.79298 -122.39636 576.709848 377427.5 2555.166667
Bernal Heights NaN NaN 576.746488 379374.5 3080.333333
Downtown NaN NaN 391.434378 378401.0 2817.285714
Ingleside NaN NaN 367.895144 377427.5 2509.000000
Outer Richmond NaN NaN 473.900773 378401.0 2817.285714
In [21]:
# Call the dropna function to remove any neighbourhoods that do not have data
all_neighbourhoods_df = all_neighbourhoods_df.reset_index().dropna()

# Rename the "index" column as "Neighbourhood" for use in the Visualisation
all_neighbourhoods_df = all_neighbourhoods_df.rename(columns={"index": "Neighbourhood"})

# Review the resulting DataFrame
display(all_neighbourhoods_df.head())
display(all_neighbourhoods_df.tail())
Neighbourhood Lat Lon sale_price_sqr_foot housing_units gross_rent
0 Alamo Square 37.791012 -122.402100 366.020712 378401.0 2817.285714
1 Anza Vista 37.779598 -122.443451 373.382198 379050.0 3031.833333
2 Bayview 37.734670 -122.401060 204.588623 376454.0 2318.400000
3 Bayview Heights 37.728740 -122.410980 590.792839 382295.0 3739.000000
5 Buena Vista Park 37.768160 -122.439330 452.680591 378076.5 2698.833333
Neighbourhood Lat Lon sale_price_sqr_foot housing_units gross_rent
68 West Portal 37.74026 -122.463880 498.488485 376940.75 2515.500000
69 Western Addition 37.79298 -122.435790 307.562201 377427.50 2555.166667
70 Westwood Highlands 37.73470 -122.456854 533.703935 376454.00 2250.500000
71 Westwood Park 37.73415 -122.457000 687.087575 382295.00 3959.000000
72 Yerba Buena 37.79298 -122.396360 576.709848 377427.50 2555.166667

Step 4: Using hvPlot with GeoViews enabled, create a points plot for the all_neighbourhoods_df DataFrame. Be sure to do the following:¶

  • Set the geo parameter to True.
  • Set the size parameter to “sale_price_sqr_foot”.
  • Set the color parameter to “gross_rent”.
  • Set the frame_width parameter to 700.
  • Set the frame_height parameter to 500.
  • Include a descriptive title.
In [25]:
# Create a plot to analyse neighbourhood info
all_neighbourhoods_df.hvplot.points(
    'Lon',
    'Lat',
    geo=True,
    size='sale_price_sqr_foot',
    color='gross_rent',
    alpha=0.8,
    tiles='OSM',
    frame_width=700,
    frame_height=500,
    title='All Neighbourhoods Average Sales Price Per Square Foot and Gross Rent in San Francisco')
Out[25]:
In [37]:
# Find the highest gross rent
highest_gross_rent = all_neighbourhoods_df['gross_rent'].max()
all_neighbourhoods_df.loc[all_neighbourhoods_df['gross_rent']== highest_gross_rent]
Out[37]:
Neighbourhood Lat Lon sale_price_sqr_foot housing_units gross_rent
71 Westwood Park 37.73415 -122.457 687.087575 382295.0 3959.0
In [36]:
highest_sale_price = all_neighbourhoods_df['sale_price_sqr_foot'].max()
all_neighbourhoods_df.loc[all_neighbourhoods_df['sale_price_sqr_foot'] == highest_sale_price]
Out[36]:
Neighbourhood Lat Lon sale_price_sqr_foot housing_units gross_rent
65 Union Square District 37.79101 -122.4021 903.993258 377427.5 2555.166667

Step 5: Use the interactive map to answer the following question:¶

Question: Which neighbourhood has the highest gross rent, and which has the highest sale price per square foot?

Answer: Westwood Park neighbourhood has the highest gross rent. Union Square District has the highest sale price per square foot

Compose Your Data Story¶

Based on the visualisations that you created, answer the following questions:

Question: How does the trend in rental income growth compare to the trend in sales prices? Does this same trend hold true for all the neighbourhoods across San Francisco?

Answer: In general, sales prices were gradually increased in a slow trend, while rental income was raised more aggressively, the growth rate was steeply increased during the period of 2010 and 2016. For all the neighbourhoods across San Francisco, they are not all in the same trend. Some of them were in the same trend such as Buena Vista Park, Central Richmond and Central Sunset. Each neighbourhood were different, for example, Potrero Hill, Park North and Oceanview sales prices were decreased between 2015 and 2016 while rental income still increased.

Question: What insights can you share with your company about the potential one-click, buy-and-rent strategy that they're pursuing? Do neighbourhoods exist that you would suggest for investment, and why?

Answer: For the whole market in San Francisco, the rental market are always in high demand no matter the house trading selling market are hot or cooldown. Sales price did not affect rental income. They are different. Also, location/neighbourhoods are an important impact that influence rental and housing selling market. It still depends on the neighbourhood you choose. If someone really need to purchase or rental a place, we should analyze the detail by neighbourhoods, it is not accurate to only overlook the general performance for the city of San Francisco. A city cannot represent all neighbourhoods.

I would suggest to invest the neighbourhoods that have higher populations. Miraloma Park, Corona Heights and Forest Knolls are in my recommendation lists for investments, because they were always in positive trend in both rental and housing trading market for these 6 years 2010-2016. That means those neighbourhoods are already in high demand and good performance in real estate, local people are generally picked those area to live. These are popular neighbourhoods.

In [ ]: